Sber and Skoltech unveil TOHA, a cheap attention-topology method for detecting RAG hallucinations (ACL 2026)
Sber/Skoltech (LARSS joint lab)
Researchers of Sberbank's Center for Practical AI and Skoltech proposed TOHA (TOpology-based HAllucination detector), which flags LLM answers not supported by retrieved context by analyzing topological divergence on attention-head graphs. The method needs no extra model training and only a small set of labeled examples, making it far cheaper than sampling-based detectors. The paper was published at ACL 2026 (A*-rated); co-authors include Skoltech associate professor Alexey Zaitsev, head of the Skoltech–Sberbank lab LARSS.
Why it matters
Cheap, training-free hallucination detection directly targets the main trust bottleneck of RAG-based enterprise assistants, which is exactly where Sber and other Russian labs deploy LLMs in production.
Importance: 2/5
Notable ACL 2026 paper with official press coverage